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Triangulation in Research | Guide, Types, Examples

Published on January 3, 2022 by Pritha Bhandari . Revised on June 22, 2023.

Triangulation in research means using multiple datasets, methods, theories, and/or investigators to address a research question . It’s a research strategy that can help you enhance the validity and credibility of your findings and mitigate the presence of any research biases in your work.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . If you decide on mixed methods research , you’ll always use methodological triangulation.

• Qualitative research: You conduct in-depth interviews with different groups of stakeholders, such as parents, teachers, and children.
• Quantitative research: You run an eye-tracking experiment and involve three researchers in analyzing the data.
• Mixed methods research: You conduct a quantitative survey, followed by a few (qualitative) structured interviews.

Table of contents

Types of triangulation in research, what is the purpose of triangulation, pros and cons of triangulation in research, other interesting articles, frequently asked questions about triangulation.

There are four main types of triangulation:

• Data triangulation: Using data from different times, spaces, and people
• Investigator triangulation: Involving multiple researchers in collecting or analyzing data
• Theory triangulation: Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

We’ll walk you through the four types of triangulation using an example. This example is based on a real study .

Methodological triangulation

When you use methodological triangulation, you use different methods to approach the same research question.

This is the most common type of triangulation, and researchers often combine qualitative and quantitative research methods in a single study.

Methodological triangulation is useful because you avoid the flaws and research bias that come with reliance on a single research technique.

Data triangulation

In data triangulation, you use multiple data sources to answer your research question. You can vary your data collection across time, space, or different people.

When you collect data from different samples, places, or times, your results are more likely to be generalizable to other situations.

Investigator triangulation

With investigator triangulation, you involve multiple observers or researchers to collect, process, or analyze data separately.

They review video recordings of your participants playing team games in pairs and analyze and note down any cooperative behaviors. You check that their code sheets line up with each other to ensure high interrater reliability.

Investigator triangulation helps you reduce the risk of observer bias and other experimenter biases.

Theory triangulation

Triangulating theory means applying several different theoretical frameworks in your research instead of approaching a research question from just one theoretical perspective.

• People cooperate for a sense of reward: they cooperate to feel good.
• People cooperate to avoid guilt: they cooperate to avoid feeling bad.

Testing competing hypotheses is one way to perform theory triangulation. Using theory triangulation may help you understand a research problem from different perspectives or reconcile contradictions in your data.

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Researchers use triangulation for a more holistic perspective on a specific research question. Triangulation is also helpful for enhancing credibility and validity.

To cross-check evidence

It’s important to gather high-quality data for rigorous research. When you have data from only one source or investigator, it may be difficult to say whether the data are trustworthy.

But if data from multiple sources or investigators line up, you can be more certain of their credibility.

Credibility is about how confident you can be that your findings reflect reality. The more your data converge, or or agree with each other, the more credible your results will be.

For a complete picture

Triangulation helps you get a more complete understanding of your research problem.

When you rely on only one data source, methodology, or investigator, you may risk bias in your research. Observer bias may occur when there’s only one researcher collecting data. Similarly, using just one methodology means you may be disadvantaged by the inherent flaws and limitations of that method.

• Behavioral observations from a lab setting
• Self-report survey data from participants reflecting on their daily lives
• Neural data from an fMRI scanner during a cooperative task

It’s helpful to use triangulation when you want to capture the complexity of real-world phenomena. By varying your data sources, theories, and methodologies, you gain insights into the research problem from multiple perspectives and levels.

To enhance validity

Validity is about how accurately a method measures what it’s supposed to measure.

You can increase the validity of your research through triangulation. Since each method has its own strengths and weaknesses, you can combine complementary methods that account for each other’s limitations.

In contrast, survey data offers you more insights into everyday behaviors outside a lab setting, but since it’s self-reported, it may be biased.

Finally, fMRI data can tell you more about hidden neural mechanisms without any participant interference. But this type of data is only valuable for your research when combined with the others.

Like all research strategies, triangulation has both advantages and disadvantages.

Reduces bias

Triangulating data, methods, investigators, or theories helps you avoid the research bias that comes with using a single perspective in your research. You’ll get a well-rounded look into the research topic when you use triangulation.

Establishes credibility and validity

Combining different methods, data sources, and theories enhances the credibility and validity of your research. You’ll be able to trust that your data reflect real life more closely when you gather them using multiple perspectives and techniques.

Time-consuming

Triangulation can be very time-consuming and labor-intensive. You’ll need to juggle different datasets, sources, and methodologies to answer one research question.

This type of research often involves an interdisciplinary team and a higher cost and workload. You’ll need to weigh your options and strike a balance based on your time frame and research needs.

Inconsistent

Sometimes, the data from different sources, investigators, methods may not line up to give you a clear picture. Your data may be inconsistent or contradict each other.

This doesn’t necessarily mean that your research is incoherent. Rather, you’ll need to dig deeper to make sense of why your data are contradictory. These inconsistencies can be challenging but may also lead to new avenues for further research.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research

Triangulation can help:

• Reduce research bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

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The use of triangulation in qualitative research

Affiliations.

• 1 School of Nursing, McMaster University, Hamilton, Ontario.
• 2 School of Nursing and Department of Oncology, McMaster University.
• 3 School of Nursing and the Department of Clinical Epidemiology and Biostatistics, McMaster University.
• 4 School of Nursing, McMaster University.
• 5 Department of Oncology, Faculty of Health Sciences, McMaster University, Canada.
• PMID: 25158659
• DOI: 10.1188/14.ONF.545-547

Triangulation refers to the use of multiple methods or data sources in qualitative research to develop a comprehensive understanding of phenomena (Patton, 1999). Triangulation also has been viewed as a qualitative research strategy to test validity through the convergence of information from different sources. Denzin (1978) and Patton (1999) identified four types of triangulation: (a) method triangulation, (b) investigator triangulation, (c) theory triangulation, and (d) data source triangulation. The current article will present the four types of triangulation followed by a discussion of the use of focus groups (FGs) and in-depth individual (IDI) interviews as an example of data source triangulation in qualitative inquiry.

Keywords: focus groups; in-depth individual interviews; qualitative research; triangulation.

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Home » Triangulation in Research – Types, Methods and Guide

Triangulation in Research – Types, Methods and Guide

Table of Contents

Triangulation

Definition:

Triangulation is a research technique that involves the use of multiple methods or sources of data to increase the validity and reliability of findings.

When triangulated, data from different sources can be combined and analyzed to produce a more accurate understanding of the phenomenon being studied. Triangulation can be used in both quantitative and qualitative research and can be implemented at any stage of the research process.

Types of Triangulation

There are many types of Triangulation in research but we are featuring only Five main types:

Data Triangulation

Data triangulation is the use of multiple sources of data to examine a research question or phenomenon. This can include using a variety of data collection methods, such as surveys, interviews, observations, and document analysis, to gain a more comprehensive understanding of the phenomenon. By using multiple sources of data, researchers can validate their findings and reduce the risk of bias that may occur when using a single method.

Methodological Triangulation

Methodological triangulation involves using multiple research methods to investigate a research question or phenomenon. This can include both qualitative and quantitative methods, or different types of qualitative methods, such as focus groups and interviews. By using multiple methods, researchers can strengthen their findings, as well as gain a more comprehensive understanding of the phenomenon.

Theoretical Triangulation

Theoretical triangulation involves using multiple theoretical frameworks or perspectives to analyze and interpret research findings. This can include applying different theoretical models or approaches to the same data to gain a deeper understanding of the phenomenon. The use of multiple theories can also help to validate findings and identify inconsistencies.

Investigator Triangulation

Investigator triangulation involves using multiple researchers to examine a research question or phenomenon. This can include researchers with different backgrounds, expertise, and perspectives, to reduce the risk of bias and increase the validity of the findings. It can also help to validate the findings by having multiple researchers analyze and interpret the data.

Time Triangulation

Time triangulation involves studying the same phenomenon or research question at different time points. This can include longitudinal studies that track changes over time, or retrospective studies that examine the same phenomenon at different points in the past. Time triangulation can help to identify changes or patterns in the phenomenon, as well as validate findings over time.

Triangulation Methods

Triangulation is a research technique that involves using multiple methods, sources, or perspectives to validate or corroborate research findings. Here are some common triangulation methods used in research:

Qualitative and Quantitative Methods

Triangulating between qualitative and quantitative methods involves using both types of research methods to collect data and analyze the phenomenon under investigation. This can help to strengthen the validity and reliability of the findings by providing a more comprehensive understanding of the phenomenon.

Multiple Data Sources

Triangulating between multiple data sources involves collecting data from various sources to validate the findings. This can include using data from interviews, observations, surveys, or archival records to corroborate the findings.

Multiple Researchers

Triangulating between multiple researchers involves using multiple researchers to analyze and interpret the data. This can help to ensure the findings are not biased by the perspectives of a single researcher.

Triangulating Theories

Triangulating between theories involves using multiple theoretical frameworks to analyze and interpret the data. This can help to identify inconsistencies in the findings and provide a more comprehensive understanding of the phenomenon under investigation.

Triangulating Methodologies

Triangulating between methodologies involves using multiple research methods within a single research design. For example, a study may use both qualitative and quantitative methods to investigate the same phenomenon, providing a more comprehensive understanding of the phenomenon.

Triangulating Time

Triangulating between time involves studying the same phenomenon at different points in time. This can help to identify changes in the phenomenon over time and validate the findings across time.

Triangulating Participants

Triangulating between participants involves collecting data from multiple participants with different backgrounds, experiences, or perspectives. This can help to validate the findings and provide a more comprehensive understanding of the phenomenon under investigation.

Data Collection Methods

Here are some common triangulation data collection methods used in research:

Interviews are a popular data collection method used in qualitative research. Researchers may use different types of interviews, such as structured, semi-structured, or unstructured interviews, to gather data from participants. Triangulating interviews involves conducting multiple interviews with different participants or conducting interviews with the same participants at different times to validate or corroborate the findings.

Observations

Observations involve systematically observing and recording behavior or interactions in a natural setting. Researchers may use different types of observations, such as participant observation, non-participant observation, or structured observation, to collect data. Triangulating observations involves collecting data from different observers or conducting observations at different times to validate or corroborate the findings.

Surveys involve collecting data from a large number of participants using standardized questionnaires. Researchers may use different types of surveys, such as self-administered surveys or telephone surveys, to collect data. Triangulating surveys involves collecting data from different surveys or using surveys in combination with other data collection methods to validate or corroborate the findings.

Document Analysis

Document analysis involves systematically analyzing and interpreting documents, such as government reports, policy documents, or archival records, to understand a phenomenon. Triangulating document analysis involves analyzing different types of documents or using document analysis in combination with other data collection methods to validate or corroborate the findings.

Focus Groups

Focus groups involve bringing together a group of people to discuss a specific topic or phenomenon. Researchers may use different types of focus groups, such as traditional focus groups or online focus groups, to collect data. Triangulating focus groups involves conducting multiple focus groups with different participants or conducting focus groups in combination with other data collection methods to validate or corroborate the findings.

Data Analysis Methods

Here are some common data analysis methods used in triangulation:

• Comparative analysis: Comparative analysis involves comparing data collected from different sources or methods to identify similarities and differences in the findings. This can help to identify patterns and relationships across the data and validate or corroborate the findings.
• Convergent validation: Convergent validation involves using different methods to collect data on the same phenomenon and comparing the findings to identify areas of convergence or agreement. This can help to increase the validity and reliability of the findings by providing multiple perspectives on the phenomenon.
• Divergent validation: Divergent validation involves using different methods to collect data on the same phenomenon and comparing the findings to identify areas of divergence or disagreement. This can help to identify inconsistencies in the findings and provide a more comprehensive understanding of the phenomenon.
• Complementary analysis: Complementary analysis involves using different methods to collect data on different aspects of the same phenomenon and combining the findings to provide a more comprehensive understanding of the phenomenon. This can help to identify patterns and relationships across the data and provide a more complete picture of the phenomenon.
• Triangulated verification: Triangulated verification involves using multiple methods to verify the findings. This can involve using different data collection methods, data sources, or data analysis methods to validate or corroborate the findings.
• Meta-triangulation: Meta-triangulation involves using multiple studies or research designs to triangulate the findings. This can involve combining the findings from different studies or using multiple research designs to investigate the same phenomenon, providing a more comprehensive understanding of the phenomenon.
• Member checking: Member checking involves validating the findings with the participants or stakeholders involved in the research. This can help to ensure the findings accurately reflect the experiences and perspectives of the participants and increase the credibility of the findings.
• Peer review: Peer review involves having other researchers or experts review the findings to ensure their validity and reliability. This can help to identify potential biases or errors in the data analysis and increase the credibility of the findings.
• Triangulated coding: Triangulated coding involves using different coding methods or approaches to analyze the data and identify themes or patterns. This can help to ensure the reliability and validity of the coding process and increase the credibility of the findings.
• Inter-rater reliability: Inter-rater reliability involves having multiple coders independently analyze the same data and comparing their findings to ensure consistency and agreement in the coding process. This can help to increase the reliability and validity of the findings.

How to Conduct Triangulation

Here are some general steps to conduct triangulation in research:

• Determine the research question: The first step in conducting triangulation is to determine the research question or objective. This will help to identify the types of data sources and methods needed to answer the research question.
• Select multiple data sources: Identify the multiple data sources that can be used to answer the research question. These sources may include primary data sources such as surveys, interviews, or observations, or secondary data sources such as literature reviews or existing datasets.
• Choose multiple data collection methods : Choose the multiple data collection methods that can be used to gather data from each data source. These methods may include quantitative and qualitative methods, such as surveys, focus groups, interviews, or observations.
• Collect data: Collect data from each data source using the selected data collection methods. Be sure to document the methods used to collect the data and any issues that arise during the data collection process.
• Analyze data: Analyze the data using appropriate data analysis methods. This may involve using different methods or approaches to analyze the data from each data source.
• Compare and contrast findings: Compare and contrast the findings from each data source to identify similarities and differences. This can help to validate or corroborate the findings and identify any inconsistencies or biases in the data.
• Synthesize findings: Synthesize the findings from each data source to provide a more comprehensive understanding of the phenomenon under investigation. This can involve identifying patterns or themes across the data and drawing conclusions based on the findings.
• Evaluate and report findings: Evaluate the validity and reliability of the findings and report the results in a clear and concise manner. Be sure to include a description of the triangulation process and the methods used to ensure the validity and reliability of the findings.

Applications of Triangulation

Here are some common applications of triangulation:

• Validating research findings: Triangulation can be used to validate research findings by using multiple methods, sources, or perspectives to corroborate the results. This can help to ensure that the findings are accurate and reliable and increase the credibility of the research.
• Exploring complex phenomena: Triangulation can be particularly useful when investigating complex or multifaceted phenomena that cannot be fully understood using a single method or perspective. By using multiple methods or sources, triangulation can provide a more comprehensive understanding of the phenomenon under investigation.
• Enhancing data quality: Triangulation can help to enhance the quality of the data collected by identifying inconsistencies or biases in the data and providing multiple perspectives on the phenomenon. This can help to ensure that the data is accurate and reliable and increase the validity of the research.
• Providing richer data: Triangulation can provide richer and more detailed data by using multiple data collection methods or sources to capture different aspects of the phenomenon. This can provide a more complete picture of the phenomenon and help to identify patterns and relationships across the data.
• Enhancing the credibility of the research: Triangulation can enhance the credibility of the research by using multiple methods or sources to corroborate the findings and ensure their validity and reliability. This can increase the confidence that readers or stakeholders have in the research and its findings.

Examples of Triangulation

Here are some real-time examples of triangulation:

• Mixed-methods research : Mixed-methods research is a common example of triangulation that involves using both quantitative and qualitative research methods to collect and analyze data. This approach can help to validate or corroborate the findings by providing multiple perspectives on the same phenomenon.
• Clinical diagnosis : In medicine, triangulation can be used to diagnose complex or rare medical conditions. This can involve using multiple diagnostic tests, such as blood tests, imaging scans, and biopsies, to corroborate the diagnosis and ensure its accuracy.
• Market research : In market research, triangulation can be used to validate consumer preferences or opinions. This can involve using multiple data collection methods, such as surveys, focus groups, and interviews, to ensure the validity and reliability of the findings.
• Educational research: In educational research, triangulation can be used to evaluate the effectiveness of teaching methods. This can involve using multiple data sources, such as student test scores, classroom observations, and teacher interviews, to provide a more comprehensive understanding of the teaching and learning process.
• Environmental research: In environmental research, triangulation can be used to evaluate the impact of human activities on the environment. This can involve using multiple data sources, such as satellite imagery, field observations, and interviews with local communities, to provide a more comprehensive understanding of the environmental impacts.

Purpose of Triangulation

The purpose of triangulation in research is to increase the validity and reliability of the findings by using multiple data sources and methods to study the same phenomenon. Triangulation can help to mitigate the limitations of using a single data source or method and can provide a more comprehensive understanding of the research question or objective.

By using multiple data sources and methods, triangulation can help to:

• Validate research findings: Triangulation can help to validate the findings by providing converging evidence from multiple data sources and methods. This can increase the credibility of the research and reduce the likelihood of drawing false conclusions.
• Enhance the completeness of data : Triangulation can help to enhance the completeness of data by providing multiple perspectives on the same phenomenon. This can help to capture the complexity and richness of the phenomenon and reduce the risk of bias or oversimplification.
• I dentify discrepancies and inconsistencies : Triangulation can help to identify discrepancies and inconsistencies in the data by comparing and contrasting the findings from multiple data sources and methods. This can help to identify areas of uncertainty or ambiguity and guide further investigation.
• Provide a more comprehensive understanding: Triangulation can help to provide a more comprehensive understanding of the research question or objective by integrating data from multiple sources and methods. This can help to identify patterns or relationships that may not be apparent from a single data source or method.

When to use Triangulation

Here are some situations where triangulation may be appropriate:

• When the research question is complex: Triangulation may be appropriate when the research question is complex and requires a multifaceted approach. Using multiple data sources and methods can help to capture the complexity of the phenomenon under investigation.
• When the research is exploratory: Triangulation may be appropriate when the research is exploratory and aims to generate new insights or hypotheses. Using multiple data sources and methods can help to validate the findings and reduce the risk of drawing false conclusions.
• When the research is sensitive: Triangulation may be appropriate when the research is sensitive and requires a high level of rigor and validation. Using multiple data sources and methods can help to increase the credibility and rigor of the findings and reduce the likelihood of bias or error.
• When the research is interdisciplinary: Triangulation may be appropriate when the research is interdisciplinary and requires a range of expertise and methods. Using multiple data sources and methods can help to integrate different perspectives and approaches and provide a more comprehensive understanding of the phenomenon under investigation.
• When the research is longitudinal : Triangulation may be appropriate when the research is longitudinal and aims to study changes over time. Using multiple data sources and methods can help to capture the changes and validate the findings across different time periods.

Advantages of Triangulation

Here are some advantages of using triangulation:

• Increased validity: Triangulation can help to increase the validity of research findings by providing converging evidence from multiple data sources and methods. This can help to reduce the risk of drawing false conclusions and increase the credibility of the research.
• Increased reliability: Triangulation can help to increase the reliability of research findings by reducing the likelihood of bias or error. By using multiple data sources and methods, triangulation can help to validate the findings and reduce the risk of drawing incorrect conclusions.
• Enhanced completeness of data: Triangulation can help to enhance the completeness of data by providing multiple perspectives on the same phenomenon. This can help to capture the complexity and richness of the phenomenon and reduce the risk of oversimplification.
• Better understanding of the phenomenon: Triangulation can help to provide a better understanding of the phenomenon under investigation by integrating data from multiple sources and methods. This can help to identify patterns or relationships that may not be apparent from a single data source or method.
• Increased confidence in the findings: Triangulation can help to increase the confidence in the research findings by providing multiple sources of evidence. This can help to reduce the risk of drawing false conclusions and increase the credibility of the research.

Limitations of Triangulation

Here are some limitations of using triangulation:

• Resource-intensive: Triangulation can be resource-intensive in terms of time, money, and personnel. Collecting and analyzing data from multiple sources and methods can require more resources than using a single data source or method.
• Increased complexity: Triangulation can increase the complexity of the research process by requiring researchers to integrate data from multiple sources and methods. This can make the analysis more challenging and time-consuming.
• Difficulty in comparing data: Triangulation can make it difficult to compare data collected from different sources and methods. The data may be collected using different measures or instruments, making it difficult to compare or combine the data.
• Data inconsistencies: Triangulation can also result in data inconsistencies if the data collected from different sources or methods are contradictory or conflicting. This can make it challenging to interpret the findings and draw meaningful conclusions.
• Interpretation issues: Triangulation can also create interpretation issues if the findings from different data sources or methods are not consistent or do not converge. This can lead to uncertainty or ambiguity in the findings.

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Chapter 28: Triangulation

Tess Tsindos

Learning outcomes

Upon completion of this chapter, you should be able to:

• Understand the definition of triangulation.
• Describe the four types of triangulation.
• Understand how to conduct triangulation.
• Identify the strengths and limitations of triangulation.

What is triangulation?

Triangulation is the combination or blending of more than one participant group, researcher, theory and/or method in the same research. Its purpose is to understand the phenomenon under study 1 by determining consistency, or ‘truth’. 1 Triangulation can be used to demonstrate the rigour, validity and credibility of research findings. 2 While the purpose of triangulation is not to confirm results, but rather to understand differences, it can be difficult to explain inconsistent results when discussing the research undertaken.

There are four main types of triangulation 2 :

• T heoretical triangulation is the use of more than one theory to guide the research process. For example, a researcher might analyse data on family violence by applying feminist and critical theory, and they might also apply structural functionalist theory (see Section 1) when examining family violence as part of a complex system. By applying different theories, the data is able to be interrogated through theoretical lenses, which can lead to deeper understanding of the findings and greater nuance than a single theory might support.
• R esearcher triangulation is the use of multiple (two or more) researchers to collect and / or analyse data. The researchers may have different disciplinary backgrounds and experiences, and will also bring their professional and personal interpretations to the data. For example, research approaches to consumer and community involvement (or patient and public involvement) might advocate for patients to be involved in the analysis of data, to include patient perspectives in the interpretation of the data. In a study developing a ‘BroSupPORT’ portal and examining issues facing men with prostate cancer, 3 researchers found that health professionals were not sure that a Patient Reported Outcome comparator tool would be helpful in prompting health-seeking behaviour, but participants with prostate cancer welcomed such a tool. Focusing a patient lens on data in this study was important because it was able to highlight differences between perspectives of health professionals and patient participants. If only health professionals had been consulted the tool would not have been considered helpful and would have been ruled out as an option for the portal.
• Methodological triangulation is the use of multiple (two or more) methods to collect and analyse data . The data collection methods might include focus groups, interviews, photovoice, observations, field notes and more. In essence, it is bringing together the various methods used to collect data and can provide a more nuanced explanation of results. Methodological triangulation can include quantitative methods to support or harmonise results. Using quantitative and qualitative methods together enables the research to answer the questions of ‘what’ and ‘why’ (see Chapter 11: Mixed Methods). The BroSupPort portal study 3 is a good example of methodological triangulation because it used a combination of workshops, interviews and focus groups to collect data.
• Data triangulation uses more than one data source and / or method of analysis to interrogate the data. Data sources may include interviews with people in a range of roles in an organisation, rather than only those in one particular role. Data analyses might include data from both inductive and deductive perspectives. Data triangulation might also include different data sources, such as qualitative (e.g. interviews) and quantitative (e.g. surveys). In the BroSupPORT portal study 3 data were gathered at workshops, focus groups and interviews. Surveys, mind maps, River of Life activities and problem trees (in printed form), along with field notes taken at each workshop, were used to collect data. A range of techniques was used to analyse the data including, but not limited to, descriptive content analysis.

Table 28.1 provides examples of the four main types of triangulation. Other types of triangulation, such as ‘time’ and ‘space’ 3 , are not covered in this chapter because they are used less often.

Table 28.1: Examples of triangulation

How to conduct triangulation.

How triangulation is conducted depends on the type of triangulation.

• Theoretical triangulation requires an introduction to each theory and can be written as a literature review. The theories are described and then compared, to elicit inferences that will form the basis of data interpretation. For example, a feminist theory will inform data collection in such a way that girls and women (and women’s marginalised groups) will be deliberately sought out and included in the research study. Analysis would include a focus on gender identity, patriarchal oppression, diversity of culture and background, and would seek to demonstrate women’s points of view through a feminist lens. If, for example, a study is about women patients, the data collection and analysis would focus on how or whether women are represented in the data, and how women are medically treated by healthcare practitioners. Women’s own perspectives would be sought and analysed, to understand their perspectives.
• Researcher triangulation is often described in the type of data being analysed, and can often be read in the researcher’s statement of positionality or in the reflexivity section of a journal paper or report 9 . Some forms of thematic analysis (not reflexive thematic analysis) requires more than one investigator to read, re-read, code and re-code interviews or focus groups. When it is not a requirement of the method of analysis, triangulation should still be considered, in order to address concerns about the rigour, validity and credibility of findings of a single researcher. Including more than one researcher and participant can leads to greater divergence and the potential for nuanced findings.
• Methodological triangulation is used often in the literature. A decision is made about how to conduct the research, on the basis of the research question or aim. Often in mixed methods research, a qualitative component seeks to answer the question, ‘Why?’ and the quantitative component seeks to test a hypothesis or answer the question, ‘What?’. However, many qualitative methods might be included, such as interviews, focus groups, newspaper clippings, to answer the research question(s). When using methodological triangulation, the researcher is looking to expand their understanding of the findings. For example, if a survey and interviews are the mixed methods used in a study, the researcher would seek to compare and contrast the findings of both methods, to gain a comprehensive understanding of the phenomenon, and then would describe how the findings support or diverge in answering the research question(s). Thus, a study exploring barriers and enablers in the implementation of the 6-PACK falls prevention program 10 incorporated a cluster randomised control trial, economic and program evaluations, and surveys and focus groups. The findings were triangulated and results suggested that regular, practical face-to-face education and training for nurses were key to successful falls prevention program implementation in acute hospitals, as were provision of equipment; audit, reminders and feedback; leadership and champions; and the provision of falls data .
• Data triangulation involves using and analysing more than one participant group. It is often considered an aspect of methodological triangulation because different methods usually involve more than one source of data. Data collection needs to be well-defined and conducted. Once the data from all participant groups has been examined, the findings are compared and contrasted to assist in answering the research question(s).

It’s important to remember that triangulation can involve more than one type of triangulation, and this is often the case with mixed-methods research. For example, in mixed-methods research, methodological, investigator and data triangulation may be used to demonstrate the full findings of the research. While Table 28.1 has listed each type separately, examining some of the example papers will show that there is more than one type of triangulation in the studies. Strict adherence to only one triangulation type can make researching the phenomenon more difficult.

Advantages and challenges of triangulation

Comparing and contrasting theories, data sources, methods and data analyses can ensure strong reliability and validity in research results. However, this can also be time-consuming and resource-intensive. Attention needs to be paid to the nuances of the research, to provide holistic explanations. There are times when triangulation may not be considered necessary, and this also needs to be understood when addressing the research question. For example, if the purpose of the research is to develop a new theory, there may be no need to include more than one method, data point or theoretical foundation.

Triangulation is the use of more than one data source, investigator, theory or method in the same research. There are four main triangulation types: each provides a means for examining the research from different perspectives and for ensuring the rigour, validity and credibility of findings.

• Patton MQ. Enhancing the quality and credibility of qualitative analysis. Health Sciences Research . 1999:34, 1189-1208.
• Denzin NK. Sociological Methods: A Source Book (2nd ed). Mcgraw-Hill: 1978.
• Shemesh B et al. Codesigning a patient support portal with health professionals and men with prostate cancer: an action research study. Health Expect . 2022:25, 1319-1331. doi /10.1111/hex.13444
• Cohen L, Manion L, Morrison K. Research M ethods in E ducation . Routledge; 2017.
• Yeh A, Trang P. Avicii’s S.O.S.: a psychobiographical approach and corpus-based discourse analysis on suicidal ideation. Psychology of Language and Communication . 2022;26(1):207-241.  doi.10.2478/plc-2022-0010
• Lundell S, Pesola U et al .  Groping around in the dark for adequate COPD management: a qualitative study on experiences in long-term care.  BMC Health Serv Res .  2020;20:1025. doi . 10.1186/s12913-020-05875-2
• McCrone L & Kingsbury M. Combining worlds: a mixed method for understanding learning spaces. Int J Qual Methods . 2023;22.  doi : 10.1177/16094069231173781
• Johnson M, O’Hara R et al  Multiple triangulation and collaborative research using qualitative methods to explore decision making in pre-hospital emergency care. BMC Med Res Methodol . 2017;17 ( 11). doi : 10.1186/s12874-017-0290-z
• Llewellyn-Beardsley J et al “Nothing’s changed, baby”: how the mental health narratives of people with multiple and complex needs disrupt the recovery framework. SSM – Ment Health. 2023;3(100221). doi : 10.1016/j.ssmmh.2023.100221
• Ayton D et al. Barriers and enablers to the implementation of the 6-PACK falls prevention program:  pre-implementation study in hospitals participating in a cluster randomised controlled trial. PLOS ONE . 2017;12. doi: 10.1371/journal.pone.0171932

Qualitative Research – a practical guide for health and social care researchers and practitioners Copyright © 2023 by Tess Tsindos is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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The Use of Triangulation in Qualitative Research

Nancy Carter

Denise Bryant-Lukosius

Alba DiCenso

Jennifer Blythe

Alan J. Neville

Triangulation refers to the use of multiple methods or data sources in qualitative research to develop a comprehensive understanding of phenomena (Patton, 1999). Triangulation also has been viewed as a qualitative research strategy to test validity through the convergence of information from different sources. Denzin (1978) and Patton (1999) identified four types of triangulation: (a) method triangulation, (b) investigator triangulation, (c) theory triangulation, and (d) data source triangulation. The current article will present the four types of triangulation followed by a discussion of the use of focus groups (FGs) and in-depth individual (IDI) interviews as an example of data source triangulation in qualitative inquiry.

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Adams J, Bateman B, Becker F, et al. Effectiveness and acceptability of parental financial incentives and quasi-mandatory schemes for increasing uptake of vaccinations in preschool children: systematic review, qualitative study and discrete choice experiment. Southampton (UK): NIHR Journals Library; 2015 Nov. (Health Technology Assessment, No. 19.94.)

Effectiveness and acceptability of parental financial incentives and quasi-mandatory schemes for increasing uptake of vaccinations in preschool children: systematic review, qualitative study and discrete choice experiment.

Chapter 6 triangulation and integration of results.

Triangulation is a general approach whereby the convergence, complementarity and dissonance of results on related research questions, obtained from different methodological approaches, sources, theoretical perspective, or researchers are explored. It has been proposed that the validity of conclusions is enhanced if different approaches produce convergent findings.

Four types of triangulation have been described: 124

• methodological triangulation, where more than one methodological approach is used to collect data
• data triangulation, where data are collected from more than one data source or respondent group
• investigator triangulation, where two or more researchers take part in integrative analysis
• theoretical triangulation, where different theoretical perspectives or interpretative frameworks are adopted.

‘Triangulation’ is primarily used to describe the process of comparing concurrently collected qualitative findings. The term ‘integration’ has been used to describe the ‘interaction and conversation between’ findings from quantitative and qualitative components of a research programme, often conducted in series, to produce the proverbial ‘whole greater than the sum of its parts’. 125

This research programme was specifically designed to allow earlier phases to inform the development of later phases. Thus, the results of the effectiveness component of the systematic review were used to help to develop vignettes for the qualitative study, and those from the acceptability component helped to guide the focus of the qualitative study. Similarly, both the systematic review and the qualitative study informed the development of attributes and levels for the DCE. Thus, there was a degree of integration built into the programme from the start. However, an integrated consideration of the results adds further value to the programme.

Although the systematic review was the only aspect of the research that addressed the question of the effectiveness of parental financial incentives and quasi-mandatory interventions for increasing uptake of preschool vaccinations, all three studies addressed the question of the acceptability of these interventions. The triangulation and integration of findings, therefore, focuses primarily on the issue of acceptability, but this is not to the exclusion of other issues.

The first three types of triangulation listed above are particularly relevant to the present programme. A range of both quantitative (DCE, and systematic searching in the systematic review) and qualitative (focus groups with parents and carers, individual interviews with health and other professionals, and narrative synthesis in the systematic review) methods were used in the research. This diversity of methods allows methodological triangulation of the diverse perspectives provided by the three different research methods used.

As data were collected from more than one participant group (parents and carers, and health and other professionals, in the qualitative study; parents and carers who were and were not at high risk of not vaccinating their children in the DCE), data triangulation can also take place. Finally, as described below, a number of researchers took part in triangulation, leading to investigator triangulation.

The complex and problematic nature of triangulation and integration, and the absence of detailed information on how to perform them, has been identified by a number of authors. 125 , 126 We base our approach to triangulation and integration on Farmer et al. ’s ‘triangulation protocol’. 126 This involves identifying themes from each data source and method and then sorting these into similar categories. These are then ‘convergence coded’ to identify where there is agreement, silence and dissonance in terms of data from different sources and methods. Initially, this coding was performed by the lead researcher (JA). Preliminary results were then discussed, virtually, among the full research team, and the convergence coding was refined based on these discussions.

Table 19 shows a summary of the main themes identified in the research, sorted into three overall groups (financial incentives and penalties, quasi-mandatory interventions and alternative interventions to increase vaccination uptake), and then ordered to bring related themes near to each other. ‘ ✗ ’ in relevant columns indicates that a theme was identified in that research component. For this exercise, we divided the qualitative study into two components: results from parents and carers, and results from health and other professionals. Similarly, the DCE is split into two components: results from the formal DCE, and results from the wider questionnaire that supported and incorporated the DCE choice sets.

Summary of themes identified in the research, with agreement between research components identified

In Table 19 , where a theme was not identified in a particular research component, there was silence on that theme in that component. We did not identify any clear instances of dissonance with disagreement on a theme between research components. However, there are themes that could be interpreted as potentially contradictory. These are placed near each other in Table 19 and discussed further below.

Relative preferences for universal and targeted financial incentives, and quasi-mandatory interventions

There was a consistent finding across the systematic review, and both components of the qualitative study, that quasi-mandatory interventions tended to be more acceptable than parental financial incentives. Although this was not specifically tested in the DCE, the questionnaire found that quasi-mandatory interventions were more acceptable than targeted parental financial incentives, but that universal financial incentives were preferred to both quasi-mandatory and targeted incentives. The qualitative study also found that universal incentives were more acceptable than targeted incentives. It is possible that ordering effects influenced DCE participants’ responses; all participants were invited to consider quasi-mandatory interventions after having considered financial incentives interventions. However, the order in which participants in the qualitative study were invited to consider interventions varied, although preference did not. Ordering effects was not relevant to the systematic review. Overall, it seems unlikely that ordering effects are responsible for the consistent finding that quasi-mandatory interventions were preferred to parental financial incentives.

To summarise, the order of preference found for these interventions in the different research components was:

• systematic review – quasi-mandatory > financial incentives (distinction between targeted and universal financial incentives not explored)
• qualitative study (both components) – quasi-mandatory > universal financial incentives > targeted financial incentives
• DCE questionnaire component – universal financial incentives > quasi-mandatory interventions > targeted financial incentives.

The preference for universal, compared with targeted, financial incentives found in both the qualitative work and the DCE questionnaire may be related to issues of equity. The qualitative study identified that there was a general belief that any intervention should be ‘fair’, in the sense that it should be available to all. Targeting financial incentives to particular groups was considered ‘unfair’, as this would mean that only some parents would be eligible for a reward. As discussed in Chapter 5 , the idea that those parents who had delayed vaccination (i.e. acted irresponsibly) would become eligible for a financial reward under the targeted scenario was considered particularly inequitable.

The difference in relative preference for universal incentives compared with quasi-mandatory interventions found in the qualitative study and the DCE questionnaire may reflect differences in the way questions were asked, or in the setting in which preferences were elicited. The DCE questionnaire was conducted anonymously online. In contrast, although data from the qualitative study was anonymised at the analysis stage, data collection took place in a social context with an interviewer and, in the case of focus groups, other participants, present. This hints, but does not confirm, that universal parental incentives may be more acceptable than qualitative data suggest, but that people find it difficult to express this in social spaces. This could be interpreted as a form of ‘social desirability’ bias, where participants report what they feel is socially acceptable in the context, rather than their ‘true’ beliefs and attitudes. Furthermore, open and non-judgemental discussion of the acceptability of financial incentives and quasi-mandatory interventions in wider public forums (e.g. the media) may enable people to more honestly express their views about financial incentives for health behaviours in general and about preschool vaccinations in particular.

Responsible parenting, freedom to choose and ‘appropriate’ motivations for vaccination

Both parents and carers, and health and other professionals expressed concern that financial incentives could interfere with normal social expectations of ‘responsible parenting’, and that quasi-mandatory interventions might remove parents’ freedom to choose whether or not to vaccinate. The findings that parents should both be responsible and have the freedom to choose not to be responsible could, superficially, be interpreted as contradictory. However, the latter theme reflects the obvious respect that participants had for the conscious decision of some parents not to vaccinate their children. Although rarely, if ever, overtly agreeing with such a decision, participants appeared to believe that such decisions tended to be well thought through and strongly adhered to.

Furthermore, these apparently contradictory themes suggest that there is a general belief that the motivation for having one’s children vaccinated should be the desire to protect them, and others, and to act as a responsible citizen and parent. The motivation should not be the achievement of a financial reward or the avoidance of a penalty. This belief in what ‘appropriate’ motivations for health behaviours should be could be an important fundamental barrier to the widespread implementation of financial incentive and quasi-mandatory interventions. This is not a barrier that could be addressed by designing or communicating these interventions differently, but is, instead, inherent to the nature and intention of such interventions. Financial incentives and quasi-mandatory interventions are expressly designed to alter the motivation for behaviours in order to increase the likelihood they will take place.

Potential effectiveness

Previous work has found that perceived (lack of) effectiveness appears to be one important reason why financial incentive interventions are not considered acceptable. 94 – 97 However, this was not a strong finding in the current work. The systematic review was not able to draw generalisable conclusions concerning effectiveness, but did find some instances in which financial incentives were effective at encouraging uptake. Participants in the qualitative work also believed that financial incentive interventions could be effective – particularly for some specific groups of parents.

The DCE found that parents preferred financial incentives with higher values, and the questionnaire identified that 80% of those who would not require a financial incentive to vaccinate their children would, nevertheless, accept one if it was offered. Thus, although there may be a general perception that gaining financial rewards should not be the appropriate motivation for vaccination, this does not mean that people would not accept such rewards, or that rewards would not be effective in some cases. Indeed, around one-quarter of DCE questionnaire respondents stated that they would require a financial incentive to fully vaccinate their children. The potential effectiveness of financial incentives in some groups was also acknowledged in both components of the qualitative study.

Cost and cost-effectiveness

Both parents and carers, and health and other professionals expressed concerns about the cost of financial incentives and whether or not resources might be more efficiently used in other ways. Although no explicit references to cost-effectiveness were made, concerns about cost and efficiency certainly reflect this concept. In contrast, although quasi-mandatory interventions would also require substantial resources for their development and implementation, the issue of the cost and cost-effectiveness of these was not raised by participants.

Concerns about cost were not explicitly sought in the DCE or questionnaire. However, the questionnaire identified that the minimum effect level (WTA) among the minority (around one-quarter) of parents who stated that they would require a financial incentive was around £110. Most parents who would not require a financial reward to vaccinate would still accept one. The maximum acceptable level among these parents was around £70.

Cost-effectiveness may be particularly salient when considering financial incentives because of the explicit financial element in the intervention. 96 As identified in the systematic review, the cost-effectiveness of financial incentive and quasi-mandatory interventions has been very poorly studied and is not yet known. However, previous research indicates that the great majority of public health interventions meet NICE’s criteria for cost-effectiveness. 127

Alternative approaches to encouraging uptake of preschool vaccination

Participants in both components of the qualitative study made a variety of suggestions for other methods of increasing vaccination rates. Although these suggestions were spontaneous and unprompted by the researcher, they were common. In particular, both groups of participants suggested that more flexibility in the timing and location of where vaccinations were delivered and improvements in the accessibility of information and education about vaccinations and vaccine-preventable diseases would be useful.

A preference for greater flexibility in appointments was also expressed in the DCE, where out-of-hours appointments were associated with a gain in utility, but only in those parents ‘not at high risk’ of incompletely vaccinating their children. Longer waiting times were associated with a loss of utility across the board, but particularly in those parents ‘at high risk’ of incompletely vaccinating their children. Reducing waiting times during normal clinic hours may, therefore, be particularly important for increasing preschool vaccination uptake. Providing extended-hours appointments would certainly be preferred by many parents, but would be unlikely to increase uptake among those ‘at high risk’ of incomplete vaccination and so may be lower priority. One particular approach to avoid, identified in the qualitative study, was issuing ‘block’ appointments, in which a group of parents are all given the same appointment time and then made to wait until a slot is available.

Although the qualitative study found a general preference for wider availability of vaccinations, the DCE revealed a significant disutility of vaccination delivery by pharmacists and by community nurses in mobile buses, compared with vaccination provided by practice nurses in GP surgeries. This suggests that any changes to vaccination personnel and location would have to be carefully considered. It appears that parents will not trust ‘just any’ trained individual to vaccinate their children, and that some locations (e.g. pharmacies and mobile buses) are not considered appropriate. Professionals in the qualitative study also raised considerable concerns about how data on vaccination status could be shared between those working in different sectors. These issues would need to be overcome before professionals working in non-health sectors are trained to offer vaccinations. Parents in the qualitative study showed an interest in vaccination delivery in children’s centres. In the DCE, preference for vaccination delivery in children’s centres did not differ from preference for practice nurses delivering vaccinations at GP surgeries, and this could be explored further.

Although participants in the qualitative study acknowledged that substantial information on vaccinations is currently provided to new parents, there was widespread recognition that the information was not provided in a format that parents found easy to access. The DCE found a preference for information about the risks and benefits of vaccinations to be provided in numerical format rather than in charts and pictures. This preference was particularly strong in parents ‘at high risk’ of incompletely vaccinating their children. Presenting information in a range of different formats, and being sensitive to the different information needs of different parents, may help all parents to feel that their information needs are met.

This brief chapter has attempted to integrate, and triangulate, findings from the three different components of this project. By using an adapted version of the triangulation protocol method, 126 we have identified areas of overlap, as well as difference, in results from the three components. Although there were no areas of specific disagreement in the results from the different components, there were some apparent contradictions.

The use of a triangulation protocol also serves to highlight the strengths and limitations of the different methods used. The results in Table 19 indicate some of the differences in the depth and scope of the methods used. The systematic review focused on a limited number of specific questions and was able to generate evidence only on these. The small evidence base identified in the systematic review further limited the conclusions that could be drawn. In contrast, the qualitative study had a wider scope and was able to generate evidence on relevant topics not specified a priori. As in the systematic review, the DCE was able to generate evidence only on the specific a priori aims. By including a range of supporting questions in addition to the formal DCE choice set, some context and colour could be added to the DCE results. By combining these three methods, we were able to overcome some of the limitations of each individual method and provide a more holistic and nuanced understanding of the topic than if we had focused on one particular method, or disciplinary perspective, alone.

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• Cite this Page Adams J, Bateman B, Becker F, et al. Effectiveness and acceptability of parental financial incentives and quasi-mandatory schemes for increasing uptake of vaccinations in preschool children: systematic review, qualitative study and discrete choice experiment. Southampton (UK): NIHR Journals Library; 2015 Nov. (Health Technology Assessment, No. 19.94.) Chapter 6, Triangulation and integration of results.
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How to Use Triangulation in Qualitative Research To Understand Respondent Themes

Triangulating data in research involves combining multiple sources, methods, or perspectives to ensure the credibility, validity, and reliability of findings. Here's a step-by-step guide on how to triangulate data in research:

Let's say you and your insights team are looking for the answer to the question, "How do Pros shop for tools at home improvement retailers?"

To comprehensively understand the answer to this question in a data-driven way, you and your team would want to deploy strategic triangulation approaches.

What is Triangulation in Qualitative Research?

Triangulation in qualitative research refers to the practice of using multiple sources, methods, data types, researchers, theories, or perspectives to investigate a research question. The goal of triangulation is to enhance the credibility, validity, and reliability of findings by reducing bias, increasing the richness of data, and ensuring that the results are robust and well-supported.

There are several forms of triangulation that researchers can employ in qualitative research:

Data Triangulation:

This involves using multiple sources of data to examine the same phenomenon. For example, a researcher might gather information from interviews, observations, and documents to gain a comprehensive understanding of a topic.

Methodological Triangulation:

This refers to using multiple research methods to study the same research question. For instance, combining interviews with shopping homework exercises can provide a more comprehensive view of a situation.

Investigator Triangulation:

In this form, multiple researchers or observers are involved in the research process. Each researcher might bring different perspectives, biases, and interpretations to the data, leading to a more well-rounded understanding of the topic.

Theory Triangulation:

This involves using multiple theoretical frameworks to analyze the data. By approaching the data from different theoretical perspectives, researchers can gain deeper insights and uncover nuances that might otherwise be overlooked.

Time Triangulation:

This refers to studying the same phenomenon at different points in time. Comparing data collected at different time periods can reveal changes, trends, and developments over time.

The idea behind triangulation is that by combining various sources of evidence and perspectives, researchers can cross-validate their findings and mitigate the limitations inherent in any single approach. This enhances the credibility of the research and increases confidence in the interpretations drawn from the data. However, it's important to note that triangulation does not eliminate all potential biases or errors, but it does provide a more comprehensive and reliable view of the research topic.

Example of How Triangulation Could be Applied in Qualitative Research

Research question:.

Back to the original example of a research question to be answered: "How do Pros shop for tools at home improvement retailers?"

To comprehensively understand the answer to this question in a data-driven way, you and your team would want to deploy the following triangulation approaches:

Triangulation Approach:

Data Triangulation: Collect data from multiple sources. Conduct in-depth interviews with Pros, analyze their written and video reflections on a shopping assignment at specific retailers, and gather and analyze the purchase data from the retailer.

Methodological Triangulation: Use multiple research methods. Alongside interviews, administer a survey to a larger sample of Pros to quantitatively measure their purchasing behavior using the data compiled in qualitative to create the survey. This allows for a comparison between qualitative insights and quantitative trends.

Investigator Triangulation: Involve multiple researchers. Have different researchers analyze the interview transcripts independently and then come together to compare and discuss their interpretations, reducing the impact of individual biases.

Theory Triangulation: Apply different theoretical lenses. Analyze the data using theories from retailer client knowledge base, data trends and industry data to gain various perspectives on the Pro’s experiences.

Time Triangulation: Study changes among the audience segment over time. Conduct interviews and data collection at different points to capture how Pros' experiences and perceptions of the tool purchasing experience have changed or stayed constant. View the latest data from the Pro Monthly Tracker >>

By employing these forms of triangulation, the qualitative research findings will be more comprehensive and reliable. The insights from different data sources, methods, researchers, theories, and time periods will converge to provide a deeper understanding of how Pros shop for tools. This multi-faceted approach helps to confirm and enrich the interpretations drawn from the data.

How Do You Triangulate Data in Research?

1. define your research question:.

Before doing anything else, you and your insights team must clearly articulate your research question or topic of interest. This will guide the rest of the triangulation process. Read more about 7 Mistakes to Avoid When Conducting Qualitative Research Internally >>

2. Select Data Sources:

Choose different sources of data that can provide insights into your research question. These sources could include interviews, observations, surveys, documents, audiovisual materials, and more. Read more about How to Choose the Best Survey Method That Will Help You Get Results >>

3. Gather Data:

Collect data from each chosen source. Ensure that the data collection methods are appropriate for the type of data you're gathering and the research question you're exploring.

4. Analyze Data Independently:

Analyze the data from each source independently. This could involve coding, categorizing, or otherwise organizing the data to identify themes, patterns, and trends.

5. Identify Convergences and Divergences:

Compare the findings from each data source. Look for areas where the data sources converge, meaning that they provide similar insights or evidence. Also, pay attention to any divergences, where the data sources present differing perspectives or findings.

6. Integrate Findings:

Combine the findings from different data sources. This can involve creating a comprehensive synthesis of the data that highlights the key themes and patterns across sources. Address how the convergent and divergent findings contribute to a more nuanced understanding of the research question.

7. Methodological Reflection:

Reflect on the process of triangulation itself. Consider the strengths and limitations of each data source, method, or perspective. Discuss how the different sources of data complement or enrich each other.

8. Ensure Consistency:

Ensure that the interpretations and conclusions drawn from the triangulated data are consistent and coherent. This reinforces the credibility of the findings.

9. Transparent Reporting:

Clearly document the process of triangulation in your research report. Describe the data sources, methods, and strategies used to integrate findings. This transparency helps other researchers assess the rigor of your approach.

10. Discussion of Implications:

Discuss the implications of the triangulated findings. Explain how the combination of multiple data sources or perspectives has influenced the depth and breadth of your understanding of the research question.

Triangulating data requires careful planning, execution, and analysis. It's a dynamic process that enhances the validity and reliability of qualitative research by drawing on multiple sources of evidence. The goal of your research project is to present a more comprehensive and nuanced perspective on the research topic.

Getting Insights from Qualitative Market Research

Delivering actionable insights through this process is what our market intelligence team at The Farnsworth Group remains focused on, exclusively for building product manufacturers, retailers, and industry stakeholders, just as we have for roughly 35 years.

Using the most appropriate qualitative and quantitative research methodologies and modeling to answer the question(s) at hand, we provide recommendations on what your customers are looking for, so that you can be most successful in your specific market.

Simply schedule a consultation to learn more about the answers you would be able to get to your specific customer, product, and market related questions.

Learn more about the various custom market research services you can pursue with our team as well:

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Triangulation in Research | Guide, Types, Examples

Published on 8 April 2022 by Pritha Bhandari . Revised on 16 January 2023.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question . It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . If you decide on mixed methods research , you’ll always use methodological triangulation.

• Qualitative research: You conduct in-depth interviews with different groups of stakeholders, such as parents, teachers, and children.
• Quantitative research: You run an eye-tracking experiment and involve three researchers in analysing the data.
• Mixed methods research: You conduct a quantitative survey, followed by a few (qualitative) structured interviews.

Table of contents

Types of triangulation in research, what is the purpose of triangulation, pros and cons of triangulation in research, frequently asked questions about triangulation.

There are four main types of triangulation:

• Data triangulation: Using data from different times, spaces, and people
• Investigator triangulation: Involving multiple researchers in collecting or analysing data
• Theory triangulation: Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

We’ll walk you through the four types of triangulation using an example. This example is based on a real study .

Methodological triangulation

When you use methodological triangulation, you use different methods to approach the same research question.

This is the most common type of triangulation, and researchers often combine qualitative and quantitative research methods in a single study.

Methodological triangulation is useful because you avoid the flaws and research bias that come with reliance on a single research technique.

Data triangulation

In data triangulation, you use multiple data sources to answer your research question. You can vary your data collection across time, space, or different people.

When you collect data from different samples, places, or times, your results are more likely to be generalisable to other situations.

Investigator triangulation

With investigator triangulation, you involve multiple observers or researchers to collect, process, or analyse data separately.

Investigator triangulation helps you reduce the risk of observer bias and other experimenter biases.

Theory triangulation

Triangulating theory means applying several different theoretical frameworks in your research instead of approaching a research question from just one theoretical perspective.

• People cooperate for a sense of reward: they cooperate to feel good.
• People cooperate to avoid guilt: they cooperate to avoid feeling bad.

Testing competing hypotheses is one way to perform theory triangulation. Using theory triangulation may help you understand a research problem from different perspectives or reconcile contradictions in your data.

Prevent plagiarism, run a free check.

Researchers use triangulation for a more holistic perspective on a specific research question. Triangulation is also helpful for enhancing credibility and validity.

To cross-check evidence

It’s important to gather high-quality data for rigorous research. When you have data from only one source or investigator, it may be difficult to say whether the data are trustworthy.

But if data from multiple sources or investigators line up, you can be more certain of their credibility.

Credibility is about how confident you can be that your findings reflect reality. The more your data converge, or or agree with each other, the more credible your results will be.

For a complete picture

Triangulation helps you get a more complete understanding of your research problem.

When you rely on only one data source, methodology, or investigator, you may risk bias in your research. Observer bias may occur when there’s only one researcher collecting data. Similarly, using just one methodology means you may be disadvantaged by the inherent flaws and limitations of that method.

• Behavioral observations from a lab setting
• Self-report survey data from participants reflecting on their daily lives
• Neural data from an fMRI scanner during a cooperative task

It’s helpful to use triangulation when you want to capture the complexity of real-world phenomena. By varying your data sources, theories, and methodologies, you gain insights into the research problem from multiple perspectives and levels.

To enhance validity

Validity is about how accurately a method measures what it’s supposed to measure.

You can increase the validity of your research through triangulation. Since each method has its own strengths and weaknesses, you can combine complementary methods that account for each other’s limitations.

Finally, fMRI data can tell you more about hidden neural mechanisms without any participant interference. But this type of data is only valuable for your research when combined with the others.

Like all research strategies, triangulation has both advantages and disadvantages.

Reduces bias

Triangulating data, methods, investigators, or theories helps you avoid the bias that comes with using a single perspective in your research. You’ll get a well-rounded look into the research topic when you use triangulation.

Establishes credibility and validity

Combining different methods, data sources, and theories enhances the credibility and validity of your research. You’ll be able to trust that your data reflect real life more closely when you gather them using multiple perspectives and techniques.

Time-consuming

Triangulation can be very time-consuming and labour-intensive. You’ll need to juggle different datasets, sources, and methodologies to answer one research question.

This type of research often involves an interdisciplinary team and a higher cost and workload. You’ll need to weigh your options and strike a balance based on your time frame and research needs.

Inconsistency

Sometimes, the data from different sources, investigators, methods may not line up to give you a clear picture. Your data may be inconsistent or contradict each other.

This doesn’t necessarily mean that your research is incoherent. Rather, you’ll need to dig deeper to make sense of why your data are contradictory. These inconsistencies can be challenging but may also lead to new avenues for further research.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analysing data
• Theory triangulation : Using varying theoretical perspectives in your research

Triangulation can help:

• Reduce bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labour-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

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Triangulation is a research approach that combines numerous data sources, theories or research methodologies to guarantee that a research study’s data, analysis, and findings are as thorough and precise as possible.

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• Volume 16, Issue 4
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• Roberta Heale 1 ,
• Dorothy Forbes 2
• 1 School of Nursing, Laurentian University , Sudbury, Ontario , Canada
• 2 Faculty of Nursing , University of Alberta , Edmonton, Alberta , Canada
• Correspondence to : Roberta Heale School of Nursing, Laurentian University, Sudbury, ON, Canada P3E2C6; rheale{at}laurentian.ca

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The term ‘triangulation’ originates in the field of navigation where a location is determined by using the angles from two known points. 1 Triangulation in research is the use of more than one approach to researching a question. The objective is to increase confidence in the findings through the confirmation of a proposition using two or more independent measures. 2 The combination of findings from two or more rigorous approaches provides a more comprehensive picture of the results than either approach could do alone. 3

Methodological triangulation is the most common type of triangulation. 2 Studies that use triangulation may include two or more sets of data collection using the same methodology, such as from qualitative data sources. Alternatively, the study may use two different data collection methods as with qualitative and quantitative. 4 “This can allow the limitations from each method to be transcended by comparing findings from different perspectives….” 4

Triangulation is often used to describe research where two or more methods are used, known as mixed methods. Combining both quantitative and qualitative methods to answer a specific research question may result in one of the following three outcomes: (1) the results may converge and lead to the same conclusions; (2) the results may relate to different objects or phenomena but may be complementary to each other and used to supplement the individual results and (3) the results may be divergent or contradictory . Converging results aim to increase the validity through verification; complementary results highlight different aspects of the phenomenon or illustrate different phenomenon and divergent findings can lead to new and better explanations for the phenomenon under investigation. 3

Examples of triangulation, or mixed methods, are as varied as there are research studies. Nurses’ attitudes about teamwork may be collected through a survey and focus group discussion. A study to explore the reduction of blood pressure through a nutritional education programme may include a review of participant adherence to the diet changes through daily logs along with a series of BP readings. In every case, the researchers link and compare different methods related to a single research question.

Although regarded as a means to add richness and depth to a research inquiry, there are several criticisms of the use of triangulation in research. Triangulation assumes that the data from two distinct research methods are comparable and may or may not be of equal weight in the research inquiry. In addition, when two or more data sets have convergent findings, there must be caution in interpretation since it may simply mean that each of the data sets is flawed. Others 3 question whether the term triangulation has any meaning when it is so broadly defined, mixed methods is preferred. In spite of these criticisms, triangulation is generally considered to promote a more comprehensive understanding of the phenomenon under study and to enhance the rigour of a research study.

• ↵ The Institute of Navigation . (n.d.). Getting to the point. http://www.ion.org/satdiv/education/lesson6.pdf
• Tashakkori A ,
• Williamson GR

Competing interests None.

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